Transaction formalism protocol tool in infrastructure management
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop an eight-step procedure – transaction formalism protocol (TFP) – in the area of infrastructure management. The proposed TFP is developed from two perspectives: TFP Specification (conceptual) and TFP Tool (application). This paper introduces the TFP Specification and discusses the TFP Tool in detail. Design/methodology/approach To develop the proposed TFP Tool, a five-step methodology was used: identify and select existing standards, benchmark standards, link and build on these standards, develop the proposed TFP Tool and validate the protocol. Findings The TFP Specification defines each step as a function for which inputs, controls, mechanisms, tools/techniques and outputs are specified. The TFP Tool comprises a set of forms and guidance that the transaction development personnel, including transaction analysts, transaction designers, software developers, process modellers and industry experts, will use to define transactions in infrastructure management domain. Practical implications The proposed TFP Tool enables transaction development personnel to define transactions effectively and efficiently for information and communication technology (ICT)-based solutions through defining information in a structured, consistent and easy way. Originality/value The TFP Tool was built on existing standards incorporating their shortcomings, including lack of a step-by-step procedure to help guide the personnel what to do next, lack of transaction monitoring and improvement steps and lack of standardised forms to collect information in a prescribed format for implementation in ICT-based collaboration systems. The proposed Tool was evaluated and found to be feasible, usable and useful.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".